Traditional image-based skin-infection (e.g., Melanoma, Ringwork, Otitis Media, and Otitis External) detections or diagnoses require domain expertise. Skin-infection features derived from heuristics are extracted from images for conducting classification and analysis. For example, Dermatologists have developed “ABCDE” rules to help diagnose melanoma, which is the most serious type of skin cancer. Such rules are fundamentally based on the measures of size, shape and color. Otitis media (OM) detection is another good example of image-based inner-ear skin-infection diagnosis. Prominent symptoms of OM include skin inflammation, redness, bulging and a perforation of the middle ear. However, specifying such kind of human heuristic features involves a hand-crafted process, and thereby requires domain expertise. Often times, human heuristics obtained from domain experts may not be able to capture the most discriminative characteristics, and hence the extracted features cannot achieve high detection accuracy. Besides the problem of feature representation, developing a disease-diagnosis classifier also faces the challenge of limited amount of labeled training data. Under such constraint, even an effective model may fail to learn discriminative features. Inevitably, the lack of labeled data is a common issue for almost all medical analysis.